Dear MathGroup users,
I have a question which is very important for my current research, and
which involves not only Mathematica, but computer science in general.
I have experimental data which is not very noisy, a small example of
which may be downloaded here.
Basically I need to compute the derivative of this data. Here are my
options so far:
Option 1: Finite differencing. Its terrible since the noise enhances
dramatically.
Option 2: Fitting some arbitrary function. The problem is that the
general functional form of the data changes from experiment to
experiment, so it is not possible to find a function which fits
adequately in all cases.
Option 3: Savitzky-Golay filters (self-implemented in Mathematica, based
on the discussion in Numerical Recipes, 3rd Ed.). It doesn't seem to
make much of a difference; probably because my data is not really that
noisy.
Option 4: Smoothing Splines filter. I am currently using Mr. Ludsteck
package HPFilter. So far it is by far the best outcome. However, it is
not free of some wild oscillations that are clearly non-analytical and
which are giving me quite the headache.
Any suggestions are more than welcome.
I really appreciate any help I can get.
Best regards,
Gabriel Landi